We select the Mask R-CNN [1]} framework due to its ubiquitous presence in object detection and transfer learning research. Mask R-CNN is the foundation of higher complexity/higher performing systems, such as Cascade R-CNN [2]} and HTC/HTC++ [3]}, [4]}, which may improve upon the results presented here at the cost of additional complexity that is orthogonal to the goal of benchmarking transfer learning. Our choice attempts to balance (relative) simplicity complexity while providing compelling, even though not entirely state-of-the-art, results.
